public void CombineTest()
{
Accord.Math.Tools.SetupGenerator(0);
double[,] S = Matrix.Random(5000, 2);
double[,] A =
{
{ 0.25, 0.25 },
{ -0.25, 0.75 },
};
double[,] X = S.Multiply(A);
IndependentComponentAnalysis ica = new IndependentComponentAnalysis(X);
ica.Compute(2);
var result = ica.Result;
var expected = Accord.Statistics.Tools.ZScores(X);
var actual = Accord.Statistics.Tools.ZScores(ica.Combine(result));
Assert.IsTrue(expected.IsEqual(actual, 1e-4));
}
public void CombineTest2()
{
Accord.Math.Tools.SetupGenerator(0);
double[,] S = Matrix.Random(5000, 2);
double[,] A =
{
{ 0.25, 0.25 },
{ -0.25, 0.75 },
};
double[,] X = S.Multiply(A);
IndependentComponentAnalysis ica = new IndependentComponentAnalysis(X);
ica.Compute(2);
double[,] result = ica.Result;
float[][] expected = ica.Combine(result).ToSingle().ToArray(true);
float[][] actual = ica.Combine(result.ToSingle().ToArray(true));
Assert.IsTrue(expected.IsEqual(actual, 1e-4f));
}
public void CombineTest2()
{
Accord.Math.Random.Generator.Seed = 0;
double[,] S = Matrix.Random(5000, 2);
double[,] A =
{
{ 0.25, 0.25 },
{ -0.25, 0.75 },
};
double[,] X = Matrix.Multiply(S, A);
var ica = new IndependentComponentAnalysis(X);
ica.Compute(2);
double[,] result = ica.Result;
float[][] expected = ica.Combine(result).ToSingle().ToJagged(true);
float[][] actual = ica.Combine(result.ToSingle().ToJagged(true));
Assert.IsTrue(expected.IsEqual(actual, atol: 1e-4f));
}
public void ComputeTest()
{
Accord.Math.Tools.SetupGenerator(0);
// Let's create a random dataset containing
// 5000 samples of two dimensional samples.
//
double[,] source = Matrix.Random(5000, 2);
// Now, we will mix the samples the dimensions of the samples.
// A small amount of the second column will be applied to the
// first, and vice-versa.
//
double[,] mix =
{
{ 0.25, 0.25 },
{ -0.25, 0.75 },
};
// mix the source data
double[,] input = source.Multiply(mix);
// Now, we can use ICA to identify any linear mixing between the variables, such
// as the matrix multiplication we did above. After it has identified it, we will
// be able to revert the process, retrieving our original samples again
// Create a new Independent Component Analysis
var ica = new IndependentComponentAnalysis(input);
Assert.AreEqual(IndependentComponentAlgorithm.Parallel, ica.Algorithm);
// Compute it
ica.Compute();
// Now, we can retrieve the mixing and demixing matrices that were
// used to alter the data. Note that the analysis was able to detect
// this information automatically:
double[,] mixingMatrix = ica.MixingMatrix; // same as the 'mix' matrix
double[,] revertMatrix = ica.DemixingMatrix; // inverse of the 'mix' matrix
double[,] result = ica.Result;
// Verify mixing matrix
mixingMatrix = mixingMatrix.Divide(mixingMatrix.Sum().Sum());
Assert.IsTrue(mix.IsEqual(mixingMatrix, 0.008));
// Verify demixing matrix
double[,] expected =
{
{ 0.75, -0.25 },
{ 0.25, 0.25 },
};
revertMatrix = revertMatrix.Divide(revertMatrix.Sum().Sum());
Assert.IsTrue(expected.IsEqual(revertMatrix, 0.008));
}
public void ComputeTest()
{
Accord.Math.Tools.SetupGenerator(0);
// Let's create a random dataset containing
// 5000 samples of two dimensional samples.
//
double[,] source = Matrix.Random(5000, 2);
// Now, we will mix the samples the dimensions of the samples.
// A small amount of the second column will be applied to the
// first, and vice-versa.
//
double[,] mix =
{
{ 0.25, 0.25 },
{ -0.25, 0.75 },
};
// mix the source data
double[,] input = source.Multiply(mix);
// Now, we can use ICA to identify any linear mixing between the variables, such
// as the matrix multiplication we did above. After it has identified it, we will
// be able to revert the process, retrieving our original samples again
// Create a new Independent Component Analysis
var ica = new IndependentComponentAnalysis(input);
Assert.AreEqual(IndependentComponentAlgorithm.Parallel, ica.Algorithm);
// Compute it
ica.Compute();
// Now, we can retrieve the mixing and demixing matrices that were
// used to alter the data. Note that the analysis was able to detect
// this information automatically:
double[,] mixingMatrix = ica.MixingMatrix; // same as the 'mix' matrix
double[,] revertMatrix = ica.DemixingMatrix; // inverse of the 'mix' matrix
double[,] result = ica.Result;
// Verify mixing matrix
mixingMatrix = mixingMatrix.Divide(mixingMatrix.Sum().Sum());
Assert.IsTrue(mix.IsEqual(mixingMatrix, 0.008));
// Verify demixing matrix
double[,] expected =
{
{ 0.75, -0.25 },
{ 0.25, 0.25 },
};
revertMatrix = revertMatrix.Divide(revertMatrix.Sum().Sum());
Assert.IsTrue(expected.IsEqual(revertMatrix, 0.008));
}
public void SerializeTest()
{
Accord.Math.Tools.SetupGenerator(0);
double[,] source = Matrix.Random(5000, 2);
double[,] mix =
{
{ 0.25, 0.25 },
{ -0.25, 0.75 },
};
double[,] input = source.Dot(mix);
var ica = new IndependentComponentAnalysis(input);
ica.Compute();
MemoryStream stream = new MemoryStream();
{
BinaryFormatter b = new BinaryFormatter();
b.Serialize(stream, ica);
}
stream.Seek(0, SeekOrigin.Begin);
{
BinaryFormatter b = new BinaryFormatter();
ica = (IndependentComponentAnalysis)b.Deserialize(stream);
}
Assert.AreEqual(IndependentComponentAlgorithm.Parallel, ica.Algorithm);
double[][] mixingMatrix = ica.MixingMatrix; // same as the 'mix' matrix
double[][] revertMatrix = ica.DemixingMatrix; // inverse of the 'mix' matrix
double[,] result = ica.Result;
mixingMatrix = mixingMatrix.Divide(mixingMatrix.Sum());
Assert.IsTrue(mix.IsEqual(mixingMatrix, atol: 0.008));
double[,] expected =
{
{ 0.75, -0.25 },
{ 0.25, 0.25 },
};
revertMatrix = revertMatrix.Divide(revertMatrix.Sum());
Assert.IsTrue(expected.IsEqual(revertMatrix, atol: 0.008));
}
public void ComputeTest2()
{
Accord.Math.Tools.SetupGenerator(0);
double[,] S = Matrix.Random(5000, 2);
double[,] A =
{
{ 1, 1 },
{ -1, 3 },
};
A = A.Divide(Norm.Norm1(A));
double[,] X = S.Multiply(A);
IndependentComponentAnalysis ica = new IndependentComponentAnalysis(X, IndependentComponentAlgorithm.Deflation);
Assert.AreEqual(IndependentComponentAlgorithm.Deflation, ica.Algorithm);
ica.Compute(2);
var result = ica.Result;
var mixingMatrix = ica.MixingMatrix;
var revertMatrix = ica.DemixingMatrix;
// Verify mixing matrix
mixingMatrix = mixingMatrix.Divide(Norm.Norm1(mixingMatrix));
Assert.IsTrue(A.IsEqual(mixingMatrix, 0.05));
// Verify demixing matrix
double[,] expected =
{
{ 3, -1 },
{ 1, 1 },
};
expected = expected.Divide(Norm.Norm1(expected));
revertMatrix = revertMatrix.Divide(Norm.Norm1(revertMatrix));
Assert.IsTrue(expected.IsEqual(revertMatrix, 0.05));
var reverted = Accord.Statistics.Tools.ZScores(result).Abs();
var original = Accord.Statistics.Tools.ZScores(S).Abs();
Assert.IsTrue(reverted.IsEqual(original, 0.1));
}
public void ConvergenceTest()
{
IndependentComponentAnalysis ica;
// https://github.com/accord-net/framework/issues/225
var mixedData = LoadData();
ica = new IndependentComponentAnalysis(mixedData, AnalysisMethod.Standardize);
ica.Overwrite = false;
ica.Iterations = 1000;
ica.Algorithm = IndependentComponentAlgorithm.Parallel;
ica.Contrast = new Kurtosis();
ica.Compute();
Assert.AreEqual(3.2178976535060348, ica.WhiteningMatrix.Sum());
Assert.AreEqual(1, ica.MixingMatrix.Sum(), 1e-7);
}
public void ComputeTest()
{
Accord.Math.Tools.SetupGenerator(0);
double[,] S = Matrix.Random(5000, 2);
double[,] A =
{
{ 0.25, 0.25 },
{ -0.25, 0.75 },
};
double[,] X = S.Multiply(A);
IndependentComponentAnalysis ica = new IndependentComponentAnalysis(X, IndependentComponentAlgorithm.Parallel);
Assert.AreEqual(IndependentComponentAlgorithm.Parallel, ica.Algorithm);
ica.Compute(2);
var result = ica.Result;
var mixingMatrix = ica.MixingMatrix;
var revertMatrix = ica.DemixingMatrix;
// Verify mixing matrix
mixingMatrix = mixingMatrix.Divide(mixingMatrix.Sum().Sum());
Assert.IsTrue(A.IsEqual(mixingMatrix, 0.008));
// Verify demixing matrix
double[,] expected =
{
{ 0.75, -0.25 },
{ 0.25, 0.25 },
};
revertMatrix = revertMatrix.Divide(revertMatrix.Sum().Sum());
Assert.IsTrue(expected.IsEqual(revertMatrix, 0.008));
}
public void ComputeTest()
{
Accord.Math.Tools.SetupGenerator(0);
double[,] S = Matrix.Random(5000, 2);
double[,] A =
{
{ 0.25, 0.25 },
{ -0.25, 0.75 },
};
double[,] X = S.Multiply(A);
IndependentComponentAnalysis ica = new IndependentComponentAnalysis(X, IndependentComponentAlgorithm.Parallel);
Assert.AreEqual(IndependentComponentAlgorithm.Parallel, ica.Algorithm);
ica.Compute(2);
var result = ica.Result;
var mixingMatrix = ica.MixingMatrix;
var revertMatrix = ica.DemixingMatrix;
// Verify mixing matrix
mixingMatrix = mixingMatrix.Divide(mixingMatrix.Sum().Sum());
Assert.IsTrue(A.IsEqual(mixingMatrix, 0.008));
// Verify demixing matrix
double[,] expected =
{
{ 0.75, -0.25 },
{ 0.25, 0.25 },
};
revertMatrix = revertMatrix.Divide(revertMatrix.Sum().Sum());
Assert.IsTrue(expected.IsEqual(revertMatrix, 0.008));
}
public void SeparateTest()
{
Accord.Math.Tools.SetupGenerator(0);
double[,] S = Matrix.Random(5000, 2);
double[,] A =
{
{ 0.25, 0.25 },
{ -0.25, 0.75 },
};
double[,] X = Matrix.Multiply(S, A);
var ica = new IndependentComponentAnalysis(X);
ica.Compute(2);
var expected = ica.Result;
var actual = ica.Separate(X);
Assert.IsTrue(expected.IsEqual(actual, 1e-4));
}
private void btnRunAnalysis_Click(object sender, EventArgs e)
{
// Retrieve the input data as a double[,] matrix
double[,] data = input.ToMatrix(true).ToDouble();
// Create a new Independent Component Analysis
ica = new IndependentComponentAnalysis(data,
AnalysisMethod.Standardize)
{
Overwrite = true
};
// Compute the analysis
ica.Compute();
// Separate the input signals
demix = ica.Separate(input);
btnSource1.Enabled = true;
btnSource2.Enabled = true;
}
public void SeparateTest2()
{
Accord.Math.Tools.SetupGenerator(0);
double[,] S = Matrix.Random(5000, 2);
double[,] A =
{
{ 0.25, 0.25 },
{ -0.25, 0.75 },
};
double[,] X = S.Dot(A);
var ica = new IndependentComponentAnalysis(X);
ica.Compute(2);
var expected = ica.Result.ToSingle().ToJagged(true);
var actual = ica.Separate(X.ToSingle().ToJagged(true));
Assert.IsTrue(expected.IsEqual(actual, 1e-4f));
}
public void CombineTest2()
{
Accord.Math.Random.Generator.Seed = 0;
double[,] S = Matrix.Random(5000, 2);
double[,] A =
{
{ 0.25, 0.25 },
{ -0.25, 0.75 },
};
double[,] X = Matrix.Multiply(S, A);
var ica = new IndependentComponentAnalysis(X);
ica.Compute(2);
double[,] result = ica.Result;
float[][] expected = ica.Combine(result).ToSingle().ToJagged(true);
float[][] actual = ica.Combine(result.ToSingle().ToJagged(true));
Assert.IsTrue(expected.IsEqual(actual, atol: 1e-4f));
}
public void SerializeTest()
{
Accord.Math.Tools.SetupGenerator(0);
double[,] source = Matrix.Random(5000, 2);
double[,] mix =
{
{ 0.25, 0.25 },
{ -0.25, 0.75 },
};
double[,] input = source.Multiply(mix);
var ica = new IndependentComponentAnalysis(input);
ica.Compute();
MemoryStream stream = new MemoryStream();
{
BinaryFormatter b = new BinaryFormatter();
b.Serialize(stream, ica);
}
stream.Seek(0, SeekOrigin.Begin);
{
BinaryFormatter b = new BinaryFormatter();
ica = (IndependentComponentAnalysis)b.Deserialize(stream);
}
Assert.AreEqual(IndependentComponentAlgorithm.Parallel, ica.Algorithm);
double[,] mixingMatrix = ica.MixingMatrix; // same as the 'mix' matrix
double[,] revertMatrix = ica.DemixingMatrix; // inverse of the 'mix' matrix
double[,] result = ica.Result;
mixingMatrix = mixingMatrix.Divide(mixingMatrix.Sum().Sum());
Assert.IsTrue(mix.IsEqual(mixingMatrix, 0.008));
double[,] expected =
{
{ 0.75, -0.25 },
{ 0.25, 0.25 },
};
revertMatrix = revertMatrix.Divide(revertMatrix.Sum().Sum());
Assert.IsTrue(expected.IsEqual(revertMatrix, 0.008));
}
public void CombineTest()
{
Accord.Math.Tools.SetupGenerator(0);
double[,] S = Matrix.Random(5000, 2);
double[,] A =
{
{ 0.25, 0.25 },
{ -0.25, 0.75 },
};
double[,] X = S.Multiply(A);
IndependentComponentAnalysis ica = new IndependentComponentAnalysis(X);
ica.Compute(2);
var result = ica.Result;
var expected = Accord.Statistics.Tools.ZScores(X);
var actual = Accord.Statistics.Tools.ZScores(ica.Combine(result));
Assert.IsTrue(expected.IsEqual(actual, 1e-4));
}
public void SeparateTest2()
{
Accord.Math.Tools.SetupGenerator(0);
double[,] S = Matrix.Random(5000, 2);
double[,] A =
{
{ 0.25, 0.25 },
{ -0.25, 0.75 },
};
double[,] X = S.Multiply(A);
IndependentComponentAnalysis ica = new IndependentComponentAnalysis(X);
ica.Compute(2);
var expected = ica.Result.ToSingle().ToArray(true);
var actual = ica.Separate(X.ToSingle().ToArray(true));
Assert.IsTrue(expected.IsEqual(actual, 1e-4f));
}
public void ComputeTest2()
{
Accord.Math.Tools.SetupGenerator(0);
double[,] S = Matrix.Random(5000, 2);
double[,] A =
{
{ 1, 1 },
{ -1, 3 },
};
A = A.Divide(Norm.Norm1(A));
double[,] X = S.Multiply(A);
IndependentComponentAnalysis ica = new IndependentComponentAnalysis(X, IndependentComponentAlgorithm.Deflation);
Assert.AreEqual(IndependentComponentAlgorithm.Deflation, ica.Algorithm);
ica.Compute(2);
var result = ica.Result;
var mixingMatrix = ica.MixingMatrix;
var revertMatrix = ica.DemixingMatrix;
// Verify mixing matrix
mixingMatrix = mixingMatrix.Divide(Norm.Norm1(mixingMatrix));
Assert.IsTrue(A.IsEqual(mixingMatrix, 0.05));
// Verify demixing matrix
double[,] expected =
{
{ 3, -1 },
{ 1, 1 },
};
expected = expected.Divide(Norm.Norm1(expected));
revertMatrix = revertMatrix.Divide(Norm.Norm1(revertMatrix));
Assert.IsTrue(expected.IsEqual(revertMatrix, 0.05));
var reverted = Accord.Statistics.Tools.ZScores(result).Abs();
var original = Accord.Statistics.Tools.ZScores(S).Abs();
Assert.IsTrue(reverted.IsEqual(original, 0.1));
}
private void btnRunAnalysis_Click(object sender, EventArgs e)
{
// Retrieve the input data as a double[,] matrix
double[,] data = input.ToMatrix(true).ToDouble();
// Create a new Independent Component Analysis
ica = new IndependentComponentAnalysis(data,
AnalysisMethod.Standardize) { Overwrite = true };
// Compute the analysis
ica.Compute();
// Separate the input signals
demix = ica.Separate(input);
btnSource1.Enabled = true;
btnSource2.Enabled = true;
}
public void ConvergenceTest()
{
IndependentComponentAnalysis ica;
// https://github.com/accord-net/framework/issues/225
var mixedData = LoadData();
ica = new IndependentComponentAnalysis(mixedData, AnalysisMethod.Standardize);
ica.Overwrite = false;
ica.Iterations = 1000;
ica.Algorithm = IndependentComponentAlgorithm.Parallel;
ica.Contrast = new Kurtosis();
ica.Compute();
Assert.AreEqual(3.2178976535060348, ica.WhiteningMatrix.Sum());
Assert.AreEqual(1, ica.MixingMatrix.Sum(), 1e-7);
}
